Systematic Literature Review on Clinical Trial Eligibility Matching

Automation can reduce errors in a critical process: Clinical trial eligibility matching is currently labor-intensive and error-prone, but NLP technologies like named entity recognition and contextual embeddings have demonstrated "substantial improvements in screening efficiency and precision," potentially making patient recruitment safer and more reliable.

Data quality and consistency remain significant barriers: The review identifies "limitations persist regarding data completeness, annotation consistency, and model scalability across diverse clinical domains," meaning that AI systems trained on one hospital's data may not work reliably at another, limiting real-world deployment.

Explainability is essential for clinician adoption: The paper emphasizes that "explainable AI and standardized ontologies can bolster clinician trust and broaden adoption," suggesting that for doctors to actually use these AI tools, they need to understand why the system made a particular matching decision rather than treating it as a "black box."

Most research remains preliminary: While the review covers studies from 2015-2024, it notes that "rigorous prospective evaluations" are still needed, indicating that many current AI eligibility-matching systems have been tested in controlled research settings but lack real-world validation in actual clinical trial recruitment.

Standardization across healthcare systems is critical: The review highlights the importance of "standardized ontologies" for improving performance, suggesting that progress depends not just on better algorithms, but on the healthcare industry agreeing on common data formats and terminology.